Using naturalistic driving data to identify driving style based on longitudinal driving operation conditions

Purpose An individual’s driving style significantly affects overall traffic safety. However, driving style is difficult to identify due to temporal and spatial differences and scene heterogeneity of driving behavior data. As such, the study of real-time driving-style identification methods is of great significance for formulating personalized driving strategies, improving traffic safety and reducing fuel consumption. This study aims to establish a driving style recognition framework based on longitudinal driving operation conditions (DOCs) using a machine learning model and natural driving data collected by a vehicle equipped with an advanced driving assistance system (ADAS). Design/methodology/approach Specifically, a driving style recognition framework based on longitudinal DOCs was established. To train the model, a real-world driving experiment was conducted. First, the driving styles of 44 drivers were preliminarily identified through natural driving data and video data; drivers were categorized through a subjective evaluation as conservative, moderate or aggressive. Then, based on the ADAS driving data, a criterion for extracting longitudinal DOCs was developed. Third, taking the ADAS data from 47 Kms of the two test expressways as the research object, six DOCs were calibrated and the characteristic data sets of the different DOCs were extracted and constructed. Finally, four machine learning classification (MLC) models were used to classify and predict driving style based on the natural driving data. Findings The results showed that six longitudinal DOCs were calibrated according to the proposed calibration criterion. Cautious drivers undertook the largest proportion of the free cruise condition (FCC), while aggressive drivers primarily undertook the FCC, following steady condition and relative approximation condition. Compared with cautious and moderate drivers, aggressive drivers adopted a smaller time headway (THW) and distance headway (DHW). THW, time-to-collision (TTC) and DHW showed highly significant differences in driving style identification, while longitudinal acceleration (LA) showed no significant difference in driving style identification. Speed and TTC showed no significant difference between moderate and aggressive drivers. In consideration of the cross-validation results and model prediction results, the overall hierarchical prediction performance ranking of the four studied machine learning models under the current sample data set was extreme gradient boosting > multi-layer perceptron > logistic regression > support vector machine. Originality/value The contribution of this research is to propose a criterion and solution for using longitudinal driving behavior data to label longitudinal DOCs and rapidly identify driving styles based on those DOCs and MLC models. This study provides a reference for real-time online driving style identification in vehicles equipped with onboard data acquisition equipment, such as ADAS.

Traffic Crimes and risky driving: The role of personality and driving anger

The current study aims to examine the influence of personality traits (alternative Zuckerman model) and driving anger in the explanation of risky driving style in individuals convicted for road safety offences (N = 245), using as a basis an adaptation of the context-mediated model. This is a transversal, descriptive study designed to be implemented by means of surveys, in which took part 245 men convicted of road safety offences from five prisons in Galicia (a region in northwestern Spain) took part. The average age of the participants was 38.73 years (Sx-9.61), with a range between 18 and 64 years. All participants had three or more years of driving experience. Our data shows that the Impulsive-Sensation Seeking (Imp-SS) personality trait had a direct and positive effect on dangerous driving, while the Activity (Act) trait had a direct but negative effect. The Aggression-Hostility (Agg-Host) trait, in turn, influenced the risky driving style, but not directly, but by raising driving anger levels, so it acted as a powerful mediator between the Aggression-Hostility (Agg-Hos) trait and the risky driving style. In general, our research partially replicates and expands previous findings regarding the model used, the aggression-hostility personality trait (Agg-Host) was placed in the distal context, driving anger in the proximal context, while age and personality traits Activity (Act) and Impulsive-Sensation Seeking (Imp-SS) were direct predictors. The results of this study may have practical implications for the detection and rehabilitation of offenders and penalties for road safety offences.

Automation, Expert Systems Calibration of Trust in Automated Driving: A Matter of Initial Level of Trust and Automated Driving Style?

Objective Automated driving is becoming a reality, and such technology raises new concerns about human–machine interaction on road. This paper aims to investigate factors influencing trust calibration and evolution over time. Background Numerous studies showed trust was a determinant in automation use and misuse, particularly in the automated driving context. Method Sixty-one drivers participated in an experiment aiming to better understand the influence of initial level of trust (Trustful vs. Distrustful) on drivers’ behaviors and trust calibration during two sessions of simulated automated driving. The automated driving style was manipulated as positive (smooth) or negative (abrupt) to investigate human–machine early interactions. Trust was assessed over time through questionnaires. Drivers’ visual behaviors and take-over performances during an unplanned take-over request were also investigated. Results Results showed an increase of trust over time, for both Trustful and Distrustful drivers regardless the automated driving style. Trust was also found to fluctuate over time depending on the specific events handled by the automated vehicle. Take-over performances were not influenced by the initial level of trust nor automated driving style. Conclusion Trust in automated driving increases rapidly when drivers’ experience such a system. Initial level of trust seems to be crucial in further trust calibration and modulate the effect of automation performance. Long-term trust evolutions suggest that experience modify drivers’ mental model about automated driving systems. Application In the automated driving context, trust calibration is a decisive question to guide such systems’ proper utilization, and road safety.

Yellow Light Decision Based On Driving Style: Day Or Night？

Drivers' driving decisions at yellow lights are an important cause of accidents at intersections. According to previous studies driving style is an important basis for deciding whether a driver passes a yellow light or not. This study therefore aims to investigate the effect of different driving styles on driving decisions at yellow lights under different lighting conditions. In this paper, 64 licensed drivers were recruited to study the effect of different driving styles on the decision to drive through yellow lights under both daytime and nighttime lighting conditions using a driving simulator and a VR device. The results showed that maladjusted drivers were faster and more likely to pass the yellow light than adapted drivers (81.25% > 43.75%) in both day and night. Male drivers had higher overall driving style scores than female drivers, and male drivers were faster and more likely to pass a yellow light than female drivers (56.25% > 31.25%). The study also found that inexperienced drivers were faster and more likely to pass a yellow light than experienced drivers (50% > 37.5%). Overall, maladjusted drivers are more likely to pass yellow lights, and we can improve this situation by enhancing driving learning for maladjusted drivers.

Factors Influencing and Contributing to Perceived Safety of Passengers during Driverless Shuttle Rides

This study investigates the perceived safety of passengers while being on board of a driverless shuttle without a steward present. The aim of the study is to draw conclusions on factors that influence and contribute to perceived safety of passengers in driverless shuttles. For this, four different test rides were conducted, representing aspects that might challenge passengers’ perceived safety once driverless shuttles become part of public transport: passengers had to ride the shuttle on their own (without a steward present), had to interact with another passenger, and had to react to two different unexpected technical difficulties. Passengers were then asked what had influenced their perceived safety and what would contribute to it. Results show that perceived safety of passengers was high across all different test rides. The most important factors influencing the perceived safety of passengers were the shuttle’s driving style and passengers’ trust in the technology. The driving style was increasingly less important as the passengers gained experience with the driverless shuttle. Readily available contact with someone in a control room would significantly contribute to an increase in perceived safety while riding a driverless shuttle. For researchers, as well as technicians in the field of autonomous driving, our findings could inform the design and set-up of driverless shuttles in order to increase perceived safety; for example, how to signal passengers that there is always the possibility of contact to someone in a control room. Reacting to these concerns and challenges will further help to foster acceptance of AVs in society. Future research should explore our findings in an even more natural setting, e.g., a controlled mixed traffic environment.